练习1. 绘制水平柱状图 Profit by Sub-Category¶

导入数据文件'Sample - Superstore.xls'，绘制水平柱状图，展示每个商品子类别（Sub-Category）的利润（Profit），并根据利润大小显示颜色。

In [1]:
# Step1. 导入数据文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('./Sample - Superstore.xls',sheet_name='Orders')
df.head()
Out[1]:
Row ID Order ID Order Date Ship Date Ship Mode Customer ID Customer Name Segment Country City ... Postal Code Region Product ID Category Sub-Category Product Name Sales Quantity Discount Profit
0 1 CA-2017-152156 2017-11-08 2017-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420.0 South FUR-BO-10001798 Furniture Bookcases Bush Somerset Collection Bookcase 261.9600 2 0.00 41.9136
1 2 CA-2017-152156 2017-11-08 2017-11-11 Second Class CG-12520 Claire Gute Consumer United States Henderson ... 42420.0 South FUR-CH-10000454 Furniture Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 0.00 219.5820
2 3 CA-2017-138688 2017-06-12 2017-06-16 Second Class DV-13045 Darrin Van Huff Corporate United States Los Angeles ... 90036.0 West OFF-LA-10000240 Office Supplies Labels Self-Adhesive Address Labels for Typewriters b... 14.6200 2 0.00 6.8714
3 4 US-2016-108966 2016-10-11 2016-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311.0 South FUR-TA-10000577 Furniture Tables Bretford CR4500 Series Slim Rectangular Table 957.5775 5 0.45 -383.0310
4 5 US-2016-108966 2016-10-11 2016-10-18 Standard Class SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale ... 33311.0 South OFF-ST-10000760 Office Supplies Storage Eldon Fold 'N Roll Cart System 22.3680 2 0.20 2.5164

5 rows × 21 columns

In [2]:
# Step2. 数据分析：对Sub-Category进行分组，再对每组的Profit求和（数据聚合与分组操作——GroupBy机制）
data = df.groupby('Sub-Category')['Profit'].sum()
# df['Profit'].groupby(df['Sub-Category']).sum()  # 另一种写法
print(data)  # 返回Series：左侧是索引index，右侧是值values
print('\n',data.index)  # 使用.index属性查看索引
print('\n',data.values)  # 使用.values属性或者to_numpy()方法查看值（返回数组）

Sub-Category
Accessories    41936.6357
Appliances     18138.0054
Art             6527.7870
Binders        30221.7633
Bookcases      -3472.5560
Chairs         26590.1663
Copiers        55617.8249
Envelopes       6964.1767
Fasteners        949.5182
Furnishings    13059.1436
Labels          5546.2540
Machines        3384.7569
Paper          34053.5693
Phones         44515.7306
Storage        21278.8264
Supplies       -1189.0995
Tables        -17725.4811
Name: Profit, dtype: float64

 Index(['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs',
       'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels',
       'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables'],
      dtype='object', name='Sub-Category')

 [ 41936.6357  18138.0054   6527.787   30221.7633  -3472.556   26590.1663
  55617.8249   6964.1767    949.5182  13059.1436   5546.254    3384.7569
  34053.5693  44515.7306  21278.8264  -1189.0995 -17725.4811]
In [3]:
# Step3. 绘制水平柱状图
import plotly.graph_objects as go





fig.update_layout(
    title='Profit by Sub-Category',
    plot_bgcolor='white',  
    xaxis=dict(title='Profit',gridcolor='rgba(0,0,0,0.05)'),
    yaxis=dict(title='Sub-Category')
)

fig.show()
In [5]:
# 思考：如何设置一个利润阈值？柱形的颜色由是否超过阈值来决定








fig.update_layout(
    title='Profit by Sub-Category',
    plot_bgcolor='white',  
    xaxis=dict(title='Profit',gridcolor='rgba(0,0,0,0.05)'),
    yaxis=dict(title='Sub-Category'),
)

fig.show()

练习2. 绘制堆积柱状图 Profit by Sub-Category & Segment¶

导入数据文件'Sample - Superstore.xls'，使用堆积柱状图展示每个商品子类别（Sub-Category）中，不同细分客户（Segment）的利润（Profit）。

In [8]:
# Step1. 导入数据文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('./Sample - Superstore.xls',sheet_name='Orders')
df.shape,df.columns
Out[8]:
((9994, 21),
 Index(['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',
        'Customer ID', 'Customer Name', 'Segment', 'Country', 'City', 'State',
        'Postal Code', 'Region', 'Product ID', 'Category', 'Sub-Category',
        'Product Name', 'Sales', 'Quantity', 'Discount', 'Profit'],
       dtype='object'))
In [7]:
# Step2. 数据分析：从绘图角度来思考，一个图形fig中有三个trace（Segment），每个trace对应多个柱形（Sub-Category）
# 对Segment和Sub-Category两个指标进行分组，再对每组的Profit求和（数据聚合与分组操作——GroupBy机制）
data = df.groupby(['Segment','Sub-Category'])['Profit'].sum()
# df['Profit'].groupby([df['Segment'],df['Sub-Category']]).sum()  # 另一种写法
data
Out[7]:
Segment      Sub-Category
Consumer     Accessories     20735.9225
             Appliances       6981.9282
             Art              3454.3011
             Binders         17995.5972
             Bookcases       -4435.6382
             Chairs          13235.3319
             Copiers         24083.7106
             Envelopes        3264.4126
             Fasteners         576.8008
             Furnishings      7919.4227
             Labels           3075.9884
             Machines         2141.0618
             Paper           15534.6436
             Phones          23837.1147
             Storage          7104.2004
             Supplies        -1657.5513
             Tables          -9728.0378
Corporate    Accessories     12707.4805
             Appliances       7429.8952
             Art              2004.6477
             Binders          6377.3201
             Bookcases         638.4502
             Chairs           8344.6565
             Copiers         18990.2789
             Envelopes        2571.2290
             Fasteners         251.9030
             Furnishings      3508.2077
             Labels           1760.8273
             Machines          703.0190
             Paper           10361.5468
             Phones          11766.2196
             Storage          9131.0247
             Supplies          338.9264
             Tables          -4906.4986
Home Office  Accessories      8493.2327
             Appliances       3726.1820
             Art              1068.8382
             Binders          5848.8460
             Bookcases         324.6320
             Chairs           5010.1779
             Copiers         12543.8354
             Envelopes        1128.5351
             Fasteners         120.8144
             Furnishings      1631.5132
             Labels            709.4383
             Machines          540.6761
             Paper            8157.3789
             Phones           8912.3963
             Storage          5043.6013
             Supplies          129.5254
             Tables          -3090.9447
Name: Profit, dtype: float64
In [9]:
# 得到的结果是一个MultiIndex（多重索引）的Series：左侧是两层索引index，右侧是值values
# data.index  # 使用index属性查看多重索引
print(data.index.levels[0])  # 查看外层索引
print(data.index.levels[1])  # 查看内层索引
print(data['Consumer'])  # 指定任一外层索引，查看内层Series

Index(['Consumer', 'Corporate', 'Home Office'], dtype='object', name='Segment')
Index(['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs',
       'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels',
       'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables'],
      dtype='object', name='Sub-Category')
Sub-Category
Accessories    20735.9225
Appliances      6981.9282
Art             3454.3011
Binders        17995.5972
Bookcases      -4435.6382
Chairs         13235.3319
Copiers        24083.7106
Envelopes       3264.4126
Fasteners        576.8008
Furnishings     7919.4227
Labels          3075.9884
Machines        2141.0618
Paper          15534.6436
Phones         23837.1147
Storage         7104.2004
Supplies       -1657.5513
Tables         -9728.0378
Name: Profit, dtype: float64
In [10]:
# Step3. 绘制堆积柱状图
import plotly.graph_objects as go






    
fig.update(layout=dict(
    title='Profit by Sub-Category & Segment',
    barmode='stack',
))

fig.show()
In [11]:
# 思考：如何自定义颜色？

fig=go.Figure()

colors=['#76B7B2','#BAB0AC','#FF9DA7']  # 自定义颜色列表（离散值）










fig.update(layout=dict(
    title='Profit by Sub-Category & Segment',
    barmode='stack',
))

fig.show()